Status report: Data management program algorithm evaluation activity at Marshall Space Flight Center

An algorithm evaluation activity was initiated to study the problems associated with image processing by assessing the independent and interdependent effects of registration, compression, and classification techniques on LANDSAT data for several discipline applications. The objective of the activity was to make recommendations on selected applicable image processing algorithms in terms of accuracy, cost, and timeliness or to propose alternative ways of processing the data. As a means of accomplishing this objective, an Image Coding Panel was established. The conduct of the algorithm evaluation is described

Optimal Combinatorial Mechanism Design

We consider an optimal mechanism design problem with several heterogeneous objects and interdependent values. We characterize ex post incentives using an appropriate monotonicity condition and reformulate the problem in such a way that the choice of an allocation rule can be separated from the choice of the payment rule. Central to our analysis is the formulation of a regularity condition, which gives a recipe for the optimal mechanism. If the problem is regular, then an optimal mechanism can be obtained by solving a combinatorial allocation problem in which objects are allocated in a way to maximize the sum of "virtual" valuations. We identify conditions that imply regularity for two nonnested environments using the techniques of supermodular optimization.

Linkage of Optimization Models

The general goal of this article is to investigate the question of how to carry out analysis when a set of mathematical models being used are interdependent. We seek systematic ways of linking such models to each other. The linking approaches should preserve the structure of the original models so that their interpretation during the analysis does not get increasingly complicated. Although the emphasis is on linking two interdependent linear programming models, extensions to multimodel, nonlinear, and stochastic cases can, in principle, be straightforward. The article has been divided into two parts. In the first part we give a precise statement of our interdependent systems. As well, we offer three typical examples of such systems: energy supply--economy, manpower--economy, and forestry--wood processing industry interaction systems. In the second part we consider alternative approaches: classical decomposition principles, approaches derived from nondifferentiable optimization techniques, application of parametric programming techniques as well as the simplex method combined with a partitioning technique. By no means does the paper provide a final solution to our linkage problem. However, our computational experiments indicate that some of the approaches give rise to optimism, while others remain inconclusive

The effect of climate change adaptation strategies on bean yield in central and northern Uganda

This paper analyses the impact of adaptation to climate change on bean productivity on a micro-scale using instrumental variable techniques in a two-stage econometric model, using data collected from farming households in northern and central Uganda. We employed the bivariate probit technique to model simultaneous and interdependent adoption decisions, and the ordered probit to model the intensity of adaptation. We modelled the impact of adaptation using instrumental variables and the control function approach because of the potential endogeneity of the adaptation decision. The driving forces behind adoption of the two selected adaptation strategies were heterogeneous. Location-specific factors influenced the intensity of adaptation between the two study regions. The effect of adaptation was stronger for households that used a higher number of strategies, evidence that the two adaptation strategies need to be used simultaneously by farmers to maximise the positive impact of adaptation

Water Supply Planning under Interdependence of Actions: Theory and Application

An ongoing water supply planning problem in the Regional Municipality of Waterloo, Ontario, Canada, is studied to select the best water supply combination, within a multiple-objective framework, when actions are interdependent. The interdependencies in the problem are described and shown to be essential features. The problem is formulated as a multiple-criteria integer program with interdependent actions. Because of the large number of potential actions and the nonconvexity of the decision space, it is quite difficult to find nondominated subsets of actions. Instead, a modified goal programming technique is suggested to identify promising subsets. The appropriateness of this technique is explained, and the lessons learned in applying it to the Waterloo water supply planning problem are described

i2MapReduce: Incremental MapReduce for Mining Evolving Big Data

As new data and updates are constantly arriving, the results of data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. In this paper, we propose i2MapReduce, a novel incremental processing extension to MapReduce, the most widely used framework for mining big data. Compared with the state-of-the-art work on Incoop, i2MapReduce (i) performs key-value pair level incremental processing rather than task level re-computation, (ii) supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and (iii) incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. We evaluate i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics. Experimental results on Amazon EC2 show significant performance improvements of i2MapReduce compared to both plain and iterative MapReduce performing re-computation